SoroushVahidi/scidocs huggingface dataset:
Novel Word-sense Identification
(2014)

Automatic lexical acquisition has been an active area of research in computational linguistics for over two decades, but the automatic identification of new word-senses has received attention only very recently. Previous work on this topic has been limited by the availability of appropriate evaluation resources. In this paper we present the largest corpus-based dataset of diachronic sense differences to date, which we believe will encourage further work in this area. We then describe several extensions to a state-of-the-art topic modelling approach for identifying new word-senses. This adapted method shows superior performance on our dataset of two different corpus pairs to that of the original method for both: (a) types having taken on a novel sense over time; and (b) the token instances of such novel senses.
Remaining Useful Life Estimation of Critical Components With Application to Bearings
(2012)

Prognostics activity deals with the estimation of the Remaining Useful Life (RUL) of physical systems based on their current health state and their future operating conditions. RUL estimation can be done by using two main approaches, namely model-based and data-driven approaches. The first approach is based on the utilization of physics of failure models of the degradation, while the second approach is based on the transformation of the data provided by the sensors into models that represent the behavior of the degradation. This paper deals with a data-driven prognostics method, where the RUL of the physical system is assessed depending on its critical component. Once the critical component is identified, and the appropriate sensors installed, the data provided by these sensors are exploited to model the degradation's behavior. For this purpose, Mixture of Gaussians Hidden Markov Models (MoG-HMMs), represented by Dynamic Bayesian Networks (DBNs), are used as a modeling tool. MoG-HMMs allow us to represent the evolution of the component's health condition by hidden states by using temporal or frequency features extracted from the raw signals provided by the sensors. The prognostics process is then done in two phases: a learning phase to generate the behavior model, and an exploitation phase to estimate the current health state and calculate the RUL. Furthermore, the performance of the proposed method is verified by implementing prognostics performance metrics, such as accuracy, precision, and prediction horizon. Finally, the proposed method is applied to real data corresponding to the accelerated life of bearings, and experimental results are discussed.
Can you infect me now?: malware propagation in mobile phone networks
(2007)

In this paper we evaluate the effects of malware propagating usingcommunication services in mobile phone networks. Although self-propagating malware is well understood in the Internet, mobile phone networks have very different characteristics in terms of topologies, services, provisioning and capacity, devices, and communication patterns. To investigate malware in this new environment, we have developed an event-driver simulator that captures the characteristics and constraints of mobile phone networks. In particular, the simulator models realistic topologies and provisioned capacities of the network infrastructure, as well as the contactgraphs determined by cell phone address books. We evaluate the speedand severity of random contact worms in mobile phone networks, characterize the denial-of-service effects such worms could have on the network, investigate approaches to accelerate malware propagation, and discuss the implications of defending networks against such attacks.
THE EXPOSURE OF YOUTH TO UNWANTED SEXUAL MATERIAL ON THE INTERNET A National Survey of Risk , Impact , and Prevention
(2003)

This national survey of youth, ages 10 to 17, and their caretakers has several implications for the current debate about young people and Internet pornography. Twentyfive percent of youth had unwanted exposure to sexual pictures on the Internet in the past year, challenging the prevalent assumption that the problem is primarily about young people motivated to actively seek out pornography. Most youth had no negative reactions to their unwanted exposure, but one quarter said they were very or extremely upset, suggesting a priority need for more research on and interventions directed toward such negative effects. The use of filtering and blocking software was associated with a modest reduction in unwanted exposure, suggesting that it may help but is far from foolproof. Various forms of parental supervision were not associated with any reduction in exposure. The authors urge that social scientific research be undertaken to inform this highly contentious public policy controversy.
IT Security Governance: A Framework based on ISO 38500
(2015)

ISO 38500 is an international standard for IT governance. The guidelines of ISO 38500 can also be applied at the IT security functional level in order to guide the governance of IT security. This paper proposes the use of a strategic information security management (ISM) framework to implement guidelines of ISO 38500. This approach provides several strategic advantages to the organization by 1) aligning IT security initiatives to business strategy; 2) providing a mechanism for establishing and tracking security metrics; and 3) enhancing the overall maturity of business, IT and IT security processes. The framework also leverages tools such as COBIT, the Balanced Scorecard and SSE-CMM in order to implement IT security governance and continuous improvement practices. Using extant literature, this paper identifies certain challenges and solutions with respect to the governance of IT security. For practitioners, it highlights relevant links between principles of ISO 38500 and IT governance, provides an over-arching contextual framework to drive IT security governance, and demonstrates mitigation solutions for IT security governance challenges. For academics, the paper makes theoretical contributions, by relating IT security governance to business strategy and proposing that firms develop dynamic governance capabilities (Pavlou and El Sawy, 2010) or organizational learning ladders (Ciborra and Andreu, 2010).
Hierarchical Tree Long Short-Term Memory for Sentence Representations
(2018)

A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they can’t capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and longterm dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.
1 Basic model for Speech-to-Speech Translation
(2015)

This paper reviews the technology used in Speech-to-Speech Translation that is the phrases spoken in one language are immediately spoken in another language by the device. Speech-to-Speech Translation is a three step software process which includes Automatic speech Recognition, Machine Translation and voice synthesis. This paper includes the major speech translation projects using different approaches for speech recognition, translation and text to speech synthesis highlighting the major pros and cons for the approach being used.
Hyperbolic Plykin Attractor Can Exist in Neuron Models
(2005)

Hyperbolic Plykin attractor can exist in neuron models Strange hyperbolic attractors are hard to find in real physical systems. This paper provides the first example of a realistic system, a canonical three-dimensional (3D) model of bursting neurons, that is likely to have a strange hyperbolic attractor. Using a geometrical approach to the study of the neuron model, we derive a flow-defined Poincare map giving ail accurate account of the system's dynamics. In a parameter region where the neuron system undergoes bifurcations causing transitions between tonic spiking and bursting, this two-dimensional map becomes a map of a disk with several periodic holes. A particular case is the map of a disk with three holes, matching the Plykin example of a planar hyperbolic attractor. The corresponding attractor of the 3D neuron model appears to be hyperbolic (this property is not verified in the present paper) and arises as a result of a two-loop (secondary) homoclinic bifurcation of a saddle. This type of bifurcation, and the complex behavior it can produce, have not been previously examined.
Challenges and Best Practices for Mobile Application Development: Review Paper
(2017)

Over the last ten years or so, mobile devices technology has changed significantly, with these devices and operating systems becoming more sophisticated. These developments have led to a huge variety of mobile applications designed for mobile operating systems. These mobile applications are typically harder to design and build because of several factors such as screen size and limited processing power and so forth. Therefore, it is important to clearly identify the characteristics of mobile application development and the issues and challenges related to it, as well as, the key features that characterize a great mobile application which make them valuable and useful. This paper has reviewed existing literature of the challenge and best practices of mobile application development. This study contributes towards a great understanding of the characteristics of mobile application development process, examines real challenges faced and explores the best practices that can be effectively applied to improve the development of mobile application.
Friend or foe: The effect of implicit trustworthiness judgments in social decision-making
(2008)

The human face appears to play a key role in signaling social intentions and usually people form reliable and strong impressions on the basis of someone's facial appearance. Therefore, facial signals could have a substantial influence on how people evaluate and behave towards another person in a social interaction, such as an interactive risky decision-making game. Indeed, there is a growing body of evidence that demonstrates that social behavior plays a crucial role in human decision-making. Although previous research has demonstrated that explicit social information about one's partner can influence decision-making behavior, such as knowledge about the partner's moral status, much less is known about how implicit facial social cues affect strategic decision-making. One particular social cue that may be especially important in assessing how to interact with a partner is facial trustworthiness, a rapid, implicit assessment of the likelihood that the partner will reciprocate a generous gesture. In this experiment, we tested the hypothesis that implicit processing of trustworthiness is related to the degree to which participants cooperate with previously unknown partners. Participants played a Trust Game with 79 hypothetical partners who were previously rated on subjective trustworthiness. In each game, participants made a decision about how much to trust their partner, as measured by how much money they invested with that partner, with no guarantee of return. As predicted, people invested more money in partners who were subjectively rated as more trustworthy, despite no objective relationship between these factors. Moreover, the relationship between the amount of money offered seemed to be stronger for trustworthy faces as compared to untrustworthy faces. Overall, these data indicate that the perceived trustworthiness is a strong and important social cue that influences decision-making.
Assessing automotive functional safety microprocessor with ISO 26262 hardware requirements
(2014)

This paper provides a step-by-step guideline for the assessment of an automotive safety microprocessor with ISO 26262 hardware requirements. ISO 26262 part 5 - Product development at the hardware level - specifies the safety activities during the phase of the automotive hardware development. In this phase, hardware safety design is derived (from the results of ISO 26262 part 3 and 4), implemented, integrated, and tested. To prove the compliance with ISO 26262 hardware development process, quantitative evaluations on the hardware are indispensable. These quantitative evaluations are known as hardware architecture metrics and probabilistic hardware metrics. The assessment results qualify a design with an automotive safety integrity level (ASIL) which ranges from ASIL-A (lowest) to ASIL-D (highest). In this paper, we implemented an exemplary safety microprocessor to demonstrate the ISO 26262 hardware assessment process. The derivation procedures of the ASIL level from the hardware architecture metrics and probabilistic hardware metrics are fully discussed. Based on the evaluation results, we also provide design suggestions for the ISO 26262 safety hardware design.
Video retargeting: automating pan and scan
(2006)

When a video is displayed on a smaller display than originally intended, some of the information in the video is necessarily lost. In this paper, we introduce Video Retargeting that adapts video to better suit the target display, minimizing the important information lost. We define a framework that measures the preservation of the source material, and methods for estimating the important information in the video. Video retargeting crops each frame and scales it to fit the target display. An optimization process minimizes information loss by balancing the loss of detail due to scaling with the loss of content and composition due to cropping. The cropping window can be moved during a shot to introduce virtual pans and cuts, subject to constraints that ensure cinematic plausibility. We demonstrate results of adapting a variety of source videos to small display sizes.
Tutorial on agent-based modeling and simulation
(2005)

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS is a third way of doing science besides deductive and inductive reasoning. Computational advances have made possible a growing number of agent-based applications in a variety of fields. Applications range from modeling agent behavior in the stock market and supply chains, to predicting the spread of epidemics and the threat of bio-warfare, from modeling consumer behavior to understanding the fall of ancient civilizations, to name a few. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing ABMS models, and provides some thoughts on the relationship between ABMS and traditional modeling techniques.
Clickstream clustering using weighted longest common subsequences
(2001)

Categorizing visitors based on their interactions with a we bsite is a key problem in web usage mining. The clickstreams generated by various users often follow disti nct patterns, the knowledge of which may help in providing customized content. In this paper, we propose a novel and eff ective algorithm for clustering webusers based on a function of the longest common subsequence of their clickst reams that takes into account both the trajectory taken through a website and the time spent at each page. Results are presented on weblogs of www.sulekha.com to illustrate the techniques. keywords : web usage mining, clickstream, subsequence, clustering.
Multimodal language grounding for improved human-robot collaboration: exploring spatial semantic representations in the shared space of attention
(2017)

There is an increased interest in artificially intelligent technology that surrounds us and takes decisions on our behalf. This creates the need for such technology to be able to communicate with humans and understand natural language and non-verbal behaviour that may carry information about our complex physical world. Artificial agents today still have little knowledge about the physical space that surrounds us and about the objects or concepts within our attention. We are still lacking computational methods in understanding the context of human conversation that involves objects and locations around us. Can we use multimodal cues from human perception of the real world as an example of language learning for robots? Can artificial agents and robots learn about the physical world by observing how humans interact with it and how they refer to it and attend during their conversations? This PhD project’s focus is on combining spoken language and non-verbal behaviour extracted by multi-party dialogue in order to increase context awareness and spatial understanding for artificial agents.
Predicting football match results with logistic regression
(2016)

Many efforts has been made in order to predict football matches result and selecting significant variables in football. Prediction is very useful in helping managers and clubs make the right decision to win leagues and tournaments. In this paper a logistic regression model is built to predict matches results of Barclays' Premier League season 2015/2016 for home win or away win and to determine what are the significant variable to win matches. Our work is different from others as we use only significant variables gathered from researches in the same field rather than making our own guess on what are the significant variables. We also used data gathered from video game FIFA, as Shin and Gasparyan [8] showed us that including data from the video game could improve prediction quality. The model was built using variations of training data from 2010/2011 season until 2015/2016. Logistic regression is a classification method which can be used to predict sports results and it can gives additional knowledge through regression coefficients. The variables used are “Home Offense”, “Home Defense”, “Away Offense”, and “Away Defense”. We conducted experiments by altering seasons of training data used. Prediction accuracy of built model is 69.5%. We found our work improved significantly compared to the one Snyder [9] did. We concluded that the significant variables are “Home Defense”, and “Away Defense”.
An Analysis on a Dynamic Amplifier and Calibration Methods for a Pseudo-Differential Dynamic Comparator
(2012)

This paper analyzes a pseudo-differential dynamic comparator with a dynamic pre-amplifier. The transient gain of a dynamic preamplifier is derived and applied to equations of the thermal noise and the regeneration time of a comparator. This analysis enhances understanding of the roles of transistor’s parameters in pre-amplifier’s gain. Based on the calculated gain, two calibration methods are also analyzed. One is calibration of a load capacitance and the other is calibration of a bypass current. The analysis helps designers’ estimation for the accuracy of calibration, dead-zone of a comparator with a calibration circuit, and the influence of PVT variation. The analyzed comparator uses 90-nm CMOS technology as an example and each estimation is compared with simulation results. key words: dynamic amplifier, dynamic comparator, load capacitance, bypass current, calibration, and PVT variation
Development of a Wheelchair Skills Home Program for Older Adults Using a Participatory Action Design Approach
(2014)

Restricted mobility is the most common impairment among older adults and a manual wheelchair is often prescribed to address these limitations. However, limited access to rehabilitation services results in older adults typically receiving little or no mobility training when they receive a wheelchair. As an alternative and novel approach, we developed a therapist-monitored wheelchair skills home training program delivered via a computer tablet. To optimize efficacy and adherence, principles of self-efficacy and adult learning theory were foundational in the program design. A participatory action design approach was used to engage older adult wheelchair users, care providers, and prescribing clinicians in an iterative design and development process. A series of prototypes were fabricated and revised, based on feedback from eight stakeholder focus groups, until a final version was ready for evaluation in a clinical trial. Stakeholder contributions affirmed and enhanced the foundational theoretical principles and provided validation of the final product for the target population.
Mediated parent-child contact in work-separated families
(2011)

Parents and children in families living with regular separation due to work develop strategies to manage being apart. We interviewed 14 pairs of parents and children (ages 7 -- 13) from work-separated families to understand their experiences and the strategies that they use to keep their family together. Parents focus on combining scheduled synchronous and spontaneous asynchronous communication to maintain a constant presence in the life of the child. Children, on the other hand, focus on other sources of support, on other activities, and on the eventual reunion. Both the remote parent and the child rely heavily on a collocated adult to maintain aware-ness and contact. We compare work-separated families with other types of separation and highlight opportunities for new designs.
Cuckoo Search via Lévy flights
(2009)

In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.
Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems
(2011)

In this study, a new metaheuristic optimization algorithm, called cuckoo search (CS), is introduced for solving structural optimization tasks. The new CS algorithm in combination with Lévy flights is first verified using a benchmark nonlinear constrained optimization problem. For the validation against structural engineering optimization problems, CS is subsequently applied to 13 design problems reported in the specialized literature. The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area. The optimal solutions obtained by CS are mostly far better than the best solutions obtained by the existing methods. The unique search features used in CS and the implications for future research are finally discussed in detail.